/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.examples.ml;

import org.apache.spark.ml.feature.UnivariateFeatureSelector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

import java.util.Arrays;
import java.util.List;
// $example off$

/**
 * An example for UnivariateFeatureSelector.
 * Run with
 * <pre>
 * bin/run-example ml.JavaUnivariateFeatureSelectorExample
 * </pre>
 */
public class JavaUnivariateFeatureSelectorExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaUnivariateFeatureSelectorExample")
                .getOrCreate();

        // $example on$
        List<Row> data = Arrays.asList(
                RowFactory.create(1, Vectors.dense(1.7, 4.4, 7.6, 5.8, 9.6, 2.3), 3.0),
                RowFactory.create(2, Vectors.dense(8.8, 7.3, 5.7, 7.3, 2.2, 4.1), 2.0),
                RowFactory.create(3, Vectors.dense(1.2, 9.5, 2.5, 3.1, 8.7, 2.5), 3.0),
                RowFactory.create(4, Vectors.dense(3.7, 9.2, 6.1, 4.1, 7.5, 3.8), 2.0),
                RowFactory.create(5, Vectors.dense(8.9, 5.2, 7.8, 8.3, 5.2, 3.0), 4.0),
                RowFactory.create(6, Vectors.dense(7.9, 8.5, 9.2, 4.0, 9.4, 2.1), 4.0)
        );
        StructType schema = new StructType(new StructField[]{
                new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
                new StructField("features", new VectorUDT(), false, Metadata.empty()),
                new StructField("label", DataTypes.DoubleType, false, Metadata.empty())
        });

        Dataset<Row> df = spark.createDataFrame(data, schema);

        UnivariateFeatureSelector selector = new UnivariateFeatureSelector()
                .setFeatureType("continuous")
                .setLabelType("categorical")
                .setSelectionMode("numTopFeatures")
                .setSelectionThreshold(1)
                .setFeaturesCol("features")
                .setLabelCol("label")
                .setOutputCol("selectedFeatures");

        Dataset<Row> result = selector.fit(df).transform(df);

        System.out.println("UnivariateFeatureSelector output with top "
                + selector.getSelectionThreshold() + " features selected using f_classif");
        result.show();

        // $example off$
        spark.stop();
    }
}
